The R Spatial Ecosystem

“Historical” Packages

rgdal: interface between R and GDAL (Geospatial Data Abstraction Library) and PROJ4 libraries: raster / vector geospatial data formats and coordinate transformation.
sp: classes and methods for spatial data in R.
rgeos: interface between R and GEOS (Geometry Engine - Open Source) library: area, perimeter, distances, dissolve, buffer, overlap, union, contains…

These packages are still widely used.

Simple Features for R

sf Website: Simple Features for R
First release: October 20, 2016
sp, rgeos and rgdal functionnalities in one package.
Easier data handling, simpler objects.
Tidy data: compatibility with the pipe synthax and tidyverse operators.
Main author and maintainer: Edzer Pebesma (also sp author)

sf objects data structure:

format sf

Using `sf`

Data Import

library(sf)
mtq <- st_read("data/mtq/martinique.shp")

Reading layer `martinique' from data source `/home/tim/Documents/prz/satRday/lecture/data/mtq/martinique.shp' using driver `ESRI Shapefile'
Simple feature collection with 34 features and 23 fields
geometry type:  POLYGON
dimension:      XY
bbox:           xmin: 690574.4 ymin: 1592426 xmax: 736126.5 ymax: 1645660
epsg (SRID):    32620
proj4string:    +proj=utm +zone=20 +datum=WGS84 +units=m +no_defs

Projection

Get current crs with st_crs() (epsg code) and change projection with st_transform().

st_crs(mtq)

Coordinate Reference System:
  EPSG: 32620 
  proj4string: "+proj=utm +zone=20 +datum=WGS84 +units=m +no_defs"

mtq_4326 <- mtq %>% st_transform(4326)

Data Display

Default plot

plot(mtq)

Only geometry

plot(st_geometry(mtq))

Centroids Extraction

mtq_c <- st_centroid(mtq)
plot(st_geometry(mtq))
plot(st_geometry(mtq_c), add=TRUE, cex=1.2, col="red", pch=20)

Distance Matrix

mat <- st_distance(x=mtq_c,y=mtq_c)
mat[1:5,1:5]

Units: [m]
          [,1]     [,2]      [,3]      [,4]      [,5]
[1,]     0.000 35297.56  3091.501 12131.617 17136.310
[2,] 35297.557     0.00 38332.602 25518.913 18605.249
[3,]  3091.501 38332.60     0.000 15094.702 20226.198
[4,] 12131.617 25518.91 15094.702     0.000  7177.011
[5,] 17136.310 18605.25 20226.198  7177.011     0.000

Polygons Aggregation

Simple union:

mtq_u <- st_union(mtq)
plot(st_geometry(mtq), col="lightblue")
plot(st_geometry(mtq_u), add=T, lwd=2, border = "red")

Aggregation according to a grouping variable:

library(dplyr)
mtq_u2 <- mtq %>% 
  group_by(STATUT) %>% 
  summarize(P13_POP = sum(P13_POP))
plot(st_geometry(mtq), col="lightblue")
plot(st_geometry(mtq_u2), add=T, lwd=2, border = "red", col=NA)

Buffer Zone

mtq_b <- st_buffer(x = mtq_u, dist = 5000)
plot(st_geometry(mtq), col="lightblue")
plot(st_geometry(mtq_u), add=T, lwd=2)
plot(st_geometry(mtq_b), add=T, lwd=2, border = "red")

Polygon Intersection

# create a polygon
m <- rbind(c(700015,1624212), c(700015,1641586), c(719127,1641586), 
           c(719127,1624212), c(700015,1624212))
p <- st_sf(st_sfc(st_polygon(list(m))), crs = st_crs(mtq))
plot(st_geometry(mtq))
plot(p, border="red", lwd=2, add=T)

st_intersection() extracts the part of mtq that is intersected by the created polygon.

mtq_z <- st_intersection(x = mtq, y = p)
plot(st_geometry(mtq))
plot(st_geometry(mtq_z), col="red", border="green", add=T)

Count Points in Polygons

st_sample() creates a set of points contains in mtq.

pts <- st_sample(x = mtq, size = 50)
plot(st_geometry(mtq))
plot(pts, pch = 20, col = "red", add=TRUE, cex = 1)

st_interects() creates a list of the points within each polygons.

inter <- st_intersects(mtq, pts)
mtq$nbpts <- sapply(X = inter, FUN = length)
plot(st_geometry(mtq))
# display munucipalities that intersect at least 2 point
plot(st_geometry(mtq[mtq$nbpts>2,]), col = "grey", add=TRUE)
plot(pts, pch = 20, col = "red", add=TRUE, cex = 1)

Other Packages

CRAN task views aim to provide some guidance which packages on CRAN are relevant for tasks related to a certain topic.

CRAN Task View: Analysis of Spatial Data:

Classes for spatial data
Handling spatial data
Reading and writing spatial data
Visualisation
Point pattern analysis
Geostatistics
Disease mapping and areal data analysis
Spatial regression
Ecological analysis

Maps with R

Overview

Several solutions are available:

ggplot2 users can have a look to ggplot2 mapping features (geom_sf) that can mix nicely with ggspatial.
For more advanced mapping features in a ggplot2-like syntax have a look to tmap
cartography is based on base graphics and allow most of basic and advanced cartographic representations.
Full disclosure: one of the speakers is the maintainer of cartography.
mapview, leaflet and mapdeck for interactive webmaps.

Here we will focus on cartography and do small examples with ggplot2, tmap, leaflet and mapview.

Data Preparation

library(sf)
library(dplyr)
# Import geo layers
## Communes of Seine Maritime
sm <- st_read(dsn = "data/dep76/seine_maritime.geojson", 
              stringsAsFactors = FALSE, quiet=TRUE)
## French departements
dep <- st_read(dsn = "data/dep76/dep.geojson", 
               stringsAsFactors = FALSE, quiet=TRUE)

# change projection (lambert93)
sm <- st_transform(sm, 2154)
dep <- st_transform(dep, 2154)

# Import dataset  
csp <- read.csv("data/dep76/data_seine_maritime.csv")
# merge geolayer and dataset
sm <- merge(sm, csp, by="INSEE_COM", all.x=TRUE)
head(sm)

Simple feature collection with 6 features and 16 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: 526897.2 ymin: 6922528 xmax: 565312.9 ymax: 6976233
epsg (SRID):    2154
proj4string:    +proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
  INSEE_COM               LIBELLE agr art cad int emp ouv  act pagr part
1     76001 Allouville-Bellefosse   0  35  30  90 130 125  410 0.00 8.54
2     76002              Alvimare   0  13   8  55  55  72  203 0.00 6.40
3     76004           Ambrumesnil   4  16  12  59  55  47  193 2.07 8.29
4     76005 Amfreville-La-Mi-Voie   0  63 151 434 400 250 1298 0.00 4.85
   pcad  pint  pemp  pouv cat                       geometry
1  7.32 21.95 31.71 30.49 emp MULTIPOLYGON (((528381.5 69...
2  3.94 27.09 27.09 35.47 ouv MULTIPOLYGON (((528381.5 69...
3  6.22 30.57 28.50 24.35 int MULTIPOLYGON (((553154.2 69...
4 11.63 33.44 30.82 19.26 int MULTIPOLYGON (((563304.4 69...
 [ reached 'max' / getOption("max.print") -- omitted 2 rows ]

# Extract label of main cities
cities = c("Rouen","Fécamp","Le Havre","Dieppe","Le Tréport")
labels = sm %>% filter(LIBELLE %in% cities)

library(osmdata)
# Get major roads from osm
bb      <- sm %>% st_transform(4326) %>% st_bbox()
q       <- opq(bbox = bb,timeout = 180)
qm      <- add_osm_feature (q, key = 'highway',value = 'motorway', value_exact = FALSE)
qt      <- add_osm_feature (q, key = 'highway',value = 'trunk', value_exact = FALSE)
qp      <- add_osm_feature (q, key = 'highway',value = 'primary', value_exact = FALSE)

motorway<- osmdata_sf(qm)
trunk   <- osmdata_sf(qt)
primary <- osmdata_sf(qp)

roads    <- c(primary,trunk,motorway)$osm_lines %>% st_transform(st_crs(sm))
roads.geom = st_intersection(st_geometry(roads),sm) 

# Get the shape of the main river "La seine" 
qr <- q %>% 
  add_osm_feature (key = 'waterway') %>% 
  add_osm_feature(key = "name:fr", value = "La Seine")
river <- osmdata_sf(qr)
river.geom <- st_geometry(river$osm_lines %>% filter(name.fr == "La Seine")) %>%
  st_transform(st_crs(sm))

# Export road and river layers to shapefile
st_write(roads.geom, dsn = "data/dep76/road.shp")
st_write(river.geom, dsn = "data/dep76/river.shp")

bb <- st_bbox(sm)
# the bbox is used to center the map on the Seine Maritime depatement
par(mar=c(0.2,0.2,1.4,0.2), bg="azure")
plot(st_geometry(dep), col = "ivory", border="ivory3", 
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
plot(st_geometry(sm), col="cornsilk2", border = NA, lwd = 0.5, add=T)
plot(st_geometry(roads.geom),col="#666666",lwd = 1.2,add=TRUE)
plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)

`cartography`

Proportional symbols

# (Very) simple map
library(cartography)
plot(st_geometry(sm))
propSymbolsLayer(sm, var = "act")
title("Active Population")

# Custom map of active population
par(mar=c(0.2,0.2,1.4,0.2))
bb <- st_bbox(sm)
# the bbox is used to center the map on the Seine Maritime depatement
plot(st_geometry(dep), col = "ivory", border="ivory3",  bg="azure", 
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
plot(st_geometry(sm), col="cornsilk2", border = NA, lwd = 0.5, add=T)
plot(st_geometry(roads.geom),col="#666666",lwd = 1.2,add=TRUE)
plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)
propSymbolsLayer(sm, var = "act", col="darkblue", inches = 0.6, 
                 border = "white", lwd=0.7, symbols = "square",
                 legend.style = "e", legend.pos="topleft",
                 legend.title.txt = "Labor Force\n(2014)", 
                 legend.values.rnd = 0)
labelLayer(labels,txt="LIBELLE",halo=TRUE,col="white",bg="black",cex=.9) 
# Scale Bar
barscale(size = 10)
# North Arrow
north(pos = "topright", col = "darkblue")
# Full layout
layoutLayer(title = "Workforce in Seine-Maritime", 
            sources = "Insee, 2018, OpenStreetMap contributors",
            author = "Kim, Tim & Comeetie, 2019", 
            col = "darkblue", coltitle = "white", tabtitle = TRUE, 
            frame = TRUE, scale = NULL, north = FALSE)

Typology Map

# To display qualitative data
# modalities
mod <- c("agr", "art", "cad", "int", "emp", "ouv")
# labels in the legedn
modlab <- c("Agriculteurs", "Artisans","Cadres", "Prof. Inter.", "Employés", "Ouvriers")
# colors
cols <- c("#e3b4a2", "#a2d5d6", "#debbd4", "#b5dab6", "#afc2e3", "#e9e2c1")

par(mar=c(0.2,0.2,1.4,0.2))
plot(st_geometry(dep), col = "ivory", border="ivory3", bg="azure",
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
typoLayer(sm, var = "cat", 
          border = "ivory", lwd = 0.5, 
          legend.values.order = mod,
          col = cols,
          add=TRUE, legend.pos = "n")
plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)
plot(st_geometry(roads.geom),col="#666666",lwd = 1.2,add=TRUE)
# functions are dedicated to legend display
labelLayer(labels,txt="LIBELLE",halo=TRUE,col="white",bg="black",cex=.9) 
legendTypo(title.txt = "Dominant Socio-Professional\nCategory", 
           col = cols, 
           categ = modlab, 
           nodata = F)
barscale(size = 10)
north(pos = "topright", col = "darkblue")
layoutLayer(title = "Workforce Distribution in Seine-Maritime", 
            sources = "Insee, 2018, OpenStreetMap contributors",
            author = "Kim, Tim & Comeetie, 2019", 
            col = "darkblue", coltitle = "white", tabtitle = TRUE,
            frame = TRUE, scale = NULL, north = FALSE)

Choropleth Map

# Compute the share of "managers" in the active population
sm$pcad <- 100 * sm$cad / sm$act

# The getBreaks() function is used to classify the variable
bks <- getBreaks(v = sm$pcad, method = "quantile", nclass = 6)

# The carto.pal() function give access to various cartographic color palettes
cols <- carto.pal("green.pal", 3,"wine.pal",3)

# Create the map
par(mar=c(0.2,0.2,1.4,0.2))
plot(st_geometry(dep), col = "ivory", border="ivory3", bg="azure",
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
choroLayer(sm, var = "pcad", breaks = bks, 
           col = cols, border = "grey80", 
           legend.values.rnd = 1, legend.horiz = TRUE,
           lwd = 0.4, legend.pos = "topleft", 
           legend.title.txt = "Share of managers (%)", add=T)

plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)
plot(st_geometry(roads.geom),col="#666666",lwd = 1.2,add=TRUE)
# functions are dedicated to legend display
labelLayer(labels,txt="LIBELLE",halo=TRUE,col="white",bg="black",cex=.9) 
# Add a layout
layoutLayer(title = "Managers", 
            sources = "Insee, 2018, OpenStreetMap contributors",
            author = "Kim, Tim & Comeetie, 2019", 
            theme = "green.pal", 
            col = "darkred", coltitle = "white", 
            tabtitle = TRUE, 
            frame = TRUE, scale = 10)
north(pos = "topright")

We could create the same map on a cartogram based on the active population stock.

library(cartogram)
sm_c1 <- cartogram_cont(sm, "act", 3, prepare = "none")
par(mar=c(0.2,0.2,1.4,0.2))
plot(st_geometry(dep), col = "ivory", border="ivory3", bg="azure", 
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
choroLayer(sm_c1, var = "pcad", breaks = bks, 
           col = cols, border = "grey80",            
           legend.values.rnd = 1, legend.horiz = TRUE,
           lwd = 0.4, legend.pos = "topleft", 
           legend.title.txt = "Share of managers (%)", add=T)
# functions are dedicated to legend display
labelLayer(sm_c1 %>% filter(LIBELLE %in% cities), txt="LIBELLE", halo=TRUE, col="white",
           bg="black", cex=1) 
# Add a layout
layoutLayer(title = "Managers", 
            sources = "Insee, 2018",
            author = "Kim, Tim & Comeetie, 2019", 
            theme = "green.pal", 
            col = "darkred", coltitle = "white", 
            tabtitle = TRUE, 
            frame = TRUE, scale = 10)
north(pos = "topright", south = TRUE)

Gridded Map

These maps may allow to hide or, at least, diminish the MAUP.

# Create a grid based on sm (cell area = 4 square km)
grid <- getGridLayer(x = sm, cellsize = 4000*4000, 
                     type = "regular", var = c('cad', 'act'))
# Compute the share of managers
grid$pcad <- 100 * grid$cad / grid$act
# Display the map as choropleth layer
par(mar=c(0.2,0.2,1.4,0.2))
plot(st_geometry(dep), col = "ivory", border="ivory3", bg="azure", 
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
choroLayer(grid, var = "pcad", breaks=bks, 
           col = cols, border = "grey80", 
           lwd = 0.4, legend.pos = "topleft", 
           legend.title.txt = "Share of managers\n(en %)", add=T)
plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)
plot(st_geometry(roads.geom),col="#666666",lwd = 1.2,add=TRUE)
# functions are dedicated to legend display
labelLayer(labels,txt="LIBELLE",halo=TRUE,col="white",bg="black",cex=.9) 
layoutLayer(title = "Managers", 
            sources = "Insee, 2018, OpenStreetMap contributors",
            author = "Kim, Tim & Comeetie, 2019", 
            theme = "green.pal", 
            col = "darkred", coltitle = "white", 
            tabtitle = TRUE,
            frame = TRUE, scale = 10)
north(pos = "topright")

It is also possible to create hexagonal grids.

# Create a grid based on sm (cell area = 4 square km)
grid2 <- getGridLayer(x = sm, cellsize = 4000*4000, 
                     type = "hexagonal", var = c('cad', 'act'))
# Compute the share of managers
grid2$pcad <- 100 * grid2$cad / grid2$act
# Display the map as choropleth layer
par(mar=c(0.2,0.2,1.4,0.2))
plot(st_geometry(dep), col = "ivory", border="ivory3", bg="azure", 
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
choroLayer(grid2, var = "pcad", breaks=bks, 
           col = cols, border = "grey80", 
           lwd = 0.4, legend.pos = "topleft", 
           legend.title.txt = "Share of managers\n(en %)", add=T)
plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)
plot(st_geometry(roads.geom),col="#666666",lwd = 1.2,add=TRUE)
# functions are dedicated to legend display
labelLayer(labels,txt="LIBELLE",halo=TRUE,col="white",bg="black",cex=.9) 
layoutLayer(title = "Managers", 
            sources = "Insee, 2018, OpenStreetMap contributors",
            author = "Kim, Tim & Comeetie, 2019", 
            theme = "green.pal", 
            col = "darkred", coltitle = "white", 
            tabtitle = TRUE,
            frame = TRUE, scale = 10)
north(pos = "topright")

Smoothed Map

smoothLayer() uses functions from package SpatialPosition to compute Stewart’s potentials of population.
The computation of potentials could be considered as a spatial interpolation method such as inverse distance weighted interpolation (IDW) or kernel density estimator. These models aim to estimate unknown values of non-observed points from known values given by measured points. Cartographically speaking, they are often used to get a continuous surface from a set of discrete points.

grid$cad100 <- grid$cad * 100
par(mar=c(0.2,0.2,1.4,0.2), bg="azure")
plot(st_geometry(dep), col = "ivory", border="ivory3", 
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
smoothLayer(x = grid, var = "cad100", var2 = "act", 
            typefct = "exponential", 
            span = 4000, beta = 2, breaks = bks, col = cols,
            legend.pos = "topleft", mask = st_buffer(sm, 0),
            legend.values.rnd = 1,
            legend.title.txt = "Share of managers* (%)", 
            border = "grey90", lwd = 0.2, add=T)
plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)
plot(st_geometry(roads.geom),col="#666666",lwd = 1.2,add=TRUE)
# functions are dedicated to legend display
labelLayer(labels,txt="LIBELLE",halo=TRUE,col="white",bg="black",cex=.9) 
layoutLayer(title = "Managers", 
            sources = "Insee, 2018, OpenStreetMap contributors",
            author = "Kim, Tim & Comeetie, 2019", 
            theme = "green.pal", 
            col = "darkred", coltitle = "white", 
            postitle = "center",
            frame = TRUE, scale = 10)
north(pos = "topright")
text(x = 488000, y = 6921000,adj = 0,
     font = 3, cex = 0.8,
     labels = "* Potential smoothing\n  exponential function\n  span = 4 km, beta = 2")

Cheat Sheet

The cheat sheet displays a quick overview of cartography’s main features:

`ggplot2` and `tmap`

`ggplot2`

Proportional symbols layer

library(ggplot2)

labgg = labels %>% st_centroid() 
labgg$x = st_coordinates(labgg)[,1]
labgg$y = st_coordinates(labgg)[,2]

ggplot() +
  geom_sf(data = dep, colour = "ivory3",fill = "ivory") +
  geom_sf(data = river.geom, colour = "azure",size=2) +
  geom_sf(data = roads.geom, colour = "#666666",size=0.5) +
  geom_sf(data = sm %>%  st_centroid(),
          aes(size= act), colour="#E84923CC", show.legend = 'point') +
  geom_text(data=labgg,aes(x=x,y=y,label=LIBELLE))+
  scale_size(name = "Active population",
             breaks = c(100,1000,10000),
             range = c(0,20)) +
  coord_sf(crs = 2154, datum = NA,
           xlim = st_bbox(sm)[c(1,3)],
           ylim = st_bbox(sm)[c(2,4)]
  ) +
  theme_minimal() +
  theme(panel.background = element_rect(fill = "azure",color=NA)) +
  labs(title = "Active population",
       caption = "Insee, 2018\nKim & Tim, 2018",x="",y="")

Choropleth layer

ggplot() +
  geom_sf(data = dep, colour = "ivory3",fill = "ivory") +
  geom_sf(data = sm, aes(fill = pcad), colour = "grey80") +
  geom_sf(data = river.geom, colour = "azure",size=2) +
  geom_sf(data = roads.geom, colour = "#666666",size=0.5) +
  geom_text(data=labgg,aes(x=x,y=y,label=LIBELLE))+
  scale_fill_gradientn(name = "Share of managers (%)",
            
                       colours = carto.pal("green.pal", 3,"wine.pal",3),
                       values=bks/max(bks))+
  coord_sf(crs = 2154, datum = NA,
           xlim = st_bbox(sm)[c(1,3)],
           ylim = st_bbox(sm)[c(2,4)]
  ) +
  theme_minimal() +
  theme(panel.background = element_rect(fill = "azure",color=NA)) +
  labs(title = "Managers",
       caption = "Insee, 2018\nKim & Tim, 2018",x="",y="")

`tmap`

TODO Timothée

`mapview` and `leaflet`

Proportional symbols layer

These interactive maps, as appealing as they seem to be, are not really suitable for presenting geostatistical information. For porportional symbols both leaflet and mapview lack a proper way to build lengends. Nonetheless, they can be really useful for exploratory data analysis. Both librairies are quite similar with some advantage for mapview (e.g the possibility to render to canvas for bigger datasets) and some other for leaflet (e.g using a customized projection different from web-meractor)

library(mapview)
# convert polygon to point and compute the circle radius for porportional area
actifs = sm %>% st_centroid() %>% mutate(radius = sqrt(act)) %>% arrange(desc(act))
# build a map by providing a map tile provider and some options for the circles.
# with mapview the circle size will stay constant whatever the zoom level is.
mapview(actifs, map.types = "Stamen.TonerLite", cex="radius", legend=FALSE,
        col.regions="#217844", lwd=0, alpha=0.4)

With leaflet data must be provided in long/lat (st_transform(4326)) and will be converted by default to the webmercator coordinate reference system (CRS). You may choose between circles with radius specified in meters (addCircle) or in pixels (addCircleMarkers). If specified in pixel, the radius will stay the same whatever the zoom level is. If specified in meters, it will evolve with zoom level.

library(leaflet)
leaflet(actifs %>% st_transform(4326)) %>% addProviderTiles(providers$Stamen.TonerLite) %>%
  addCircles(radius = ~radius * 30, popup = ~paste0(LIBELLE," : ",act,"actifs"),
             fillColor = "#217844", stroke = FALSE, fillOpacity = 0.4)

Gridded Map

mapview and leaflet enable the use and styling of sf polygons. We may for example reuse the grid built previously to show the share of managers in Seine-Maritime. Default choropleth can be obtained with just three arguments with mapview.

mapview(grid,map.types = "Stamen.TonerLite",zcol="pcad")

leaflet enables fine tuning of the color scale and legend.

# define a color scale function from colors and breaks
color.scale <- colorBin(cols, domain = range(bks), bins=bks)

leaflet(grid  %>% st_transform(4326)) %>% addProviderTiles(providers$Stamen.TonerLite) %>%
  addPolygons(fillColor = ~color.scale(pcad), stroke = FALSE, fillOpacity = 0.7) %>% 
  addLegend(colors = cols, labels = round(bks[1:(length(bks)-1)]*10)/10,
            title = "Share of Manager (%)")

Projection

leaflet enables to use other projections than the web-mercator projection for webmaps. You may deal with tiles built with another projection system such as Lambert 93 by defining a custom leaflet crs (see gis.stackexchange resolution-from-wmts-getcapacilities-scaledenomin and mathematics-behind-converting-scale-to-resolution to deal with WMTS tiles). We use this approach here to deal with Hypsometric tints tiles (tiles colors which encode elevation) and the contour lines of chamois and ibex population area provided by the french national office of hunting and wild animals.

epsg2154 <- leafletCRS(crsClass = "L.Proj.CRS", code = "EPSG:2154",
                       proj4def = "+proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3
                       +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0
                       +units=m +no_defs",
                       resolution = 4838095.23807/cumprod(c(1,rep(2,15)))*0.00028,
                       origin= c(-357823.2365,7230727.3772)
)

tile_ortho <- "http://tiles.craig.fr/ortho/service?service=WMTS&request=GetTile&version=1.0.0&
layer=ortho_2016&style=normal&tilematrixset=lambert93&format=image%2Fjpeg&tilematrix={z}
&tilerow={y}&tilecol={x}"

tile_mnt <- "http://tiles.craig.fr/mnt/service?service=WMTS&request=GetTile&version=1.0.0&
layer=relief&style=default&tilematrixset=lambert93&format=image%2Fpng&tilematrix={z}&
tilerow={y}&tilecol={x}"

tile_attrib <- "Map data &copy;
<a href='https://www.craig.fr/contenu/1377-flux-tuile-wmswmtstms'> craig / IGN </a>
and <a href='http://www.oncfs.gouv.fr/Cartographie-ru4/Le-portail-
cartographique-de-donnees-ar291'>ONCF</a>"

bouq = read_sf("./data/BOQ_2017_massif_uni/BOQ_2017_massif_uni_L93.shp")
cha = read_sf("./data/CHA_2017_massif_uni/CHA_2017_massif_uni_L93.shp")

leaflet(options = leafletOptions(worldCopyJump = F, crs = epsg2154)) %>%
 # addTiles(urlTemplate = tile_ortho,attribution = tile_attrib) %>% 
  addTiles(urlTemplate = tile_mnt,attribution = tile_attrib) %>% 
  addPolygons(data = bouq %>% st_transform(4326), fillColor = '#50162D',color = '#772A7F',
              fillOpacity = 0.05,opacity = 1, weight = 2.5, dashArray = "3") %>% 
  addPolygons(data = cha %>% st_transform(4326), fillColor = '#88AA00',color = '#CCFF00',
              fillOpacity = 0.05,opacity = 1, weight=2.5, dashArray = "3") %>% 
  setView(2.692174, 45.067230, 4)

reproducibility

Always share your R and packages configuration !

sessionInfo()

R version 3.5.2 (2018-12-20)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux 9 (stretch)

Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.7.0
LAPACK: /usr/lib/lapack/liblapack.so.3.7.0

locale:
 [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=fr_FR.UTF-8        LC_COLLATE=fr_FR.UTF-8    
 [5] LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8   
 [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] leaflet_2.0.2         mapview_2.6.3         ggplot2_3.1.0        
 [4] SpatialPosition_1.2.0 raster_2.8-19         sp_1.3-1             
 [7] cartogram_0.1.1       cartography_2.2.1     bindrcpp_0.2.2       
[10] dplyr_0.7.8           sf_0.7-2              rmdformats_0.3.5     
[13] knitr_1.21           

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0        lattice_0.20-38   png_0.1-7        
 [4] class_7.3-14      assertthat_0.2.0  digest_0.6.18    
 [7] mime_0.6          R6_2.3.0          plyr_1.8.4       
[10] stats4_3.5.2      evaluate_0.12     e1071_1.7-0.1    
[13] highr_0.7         pillar_1.3.1      rlang_0.3.1      
[16] lazyeval_0.2.1    rstudioapi_0.9.0  miniUI_0.1.1.1   
[19] rmarkdown_1.11    labeling_0.3      webshot_0.5.1    
[22] rgdal_1.3-6       stringr_1.3.1     questionr_0.7.0  
[25] htmlwidgets_1.3   munsell_0.5.0     shiny_1.2.0      
[28] compiler_3.5.2    httpuv_1.4.5.1    xfun_0.4         
[31] pkgconfig_2.0.2   base64enc_0.1-3   rgeos_0.4-2      
[34] htmltools_0.3.6   tidyselect_0.2.5  tibble_2.0.1     
[37] bookdown_0.9      codetools_0.2-15  viridisLite_0.3.0
[40] crayon_1.3.4      withr_2.1.2       later_0.7.5      
[43] grid_3.5.2        jsonlite_1.6      satellite_1.0.1  
[46] xtable_1.8-3      gtable_0.2.0      DBI_1.0.0        
[49] magrittr_1.5      units_0.6-2       scales_1.0.0     
[52] stringi_1.2.4     promises_1.0.1    tools_3.5.2      
[55] glue_1.3.0        purrr_0.3.0       crosstalk_1.0.0  
[58] yaml_2.2.0        colorspace_1.4-0  classInt_0.3-1   
[61] bindr_0.1.1

Spatial data and cartography with R

Kim A. Etienne C. Timothée G.

SatRday Paris February 2019

The R Spatial Ecosystem

“Historical” Packages

Simple Features for R

Using `sf`

Data Import

Projection

Data Display

Centroids Extraction

Distance Matrix

Polygons Aggregation

Buffer Zone

Polygon Intersection

Count Points in Polygons

Other Packages

Maps with R

Overview

Data Preparation

`cartography`

Proportional symbols

Typology Map

Choropleth Map

Gridded Map

Smoothed Map

Cheat Sheet

`ggplot2` and `tmap`

`ggplot2`

`tmap`

`mapview` and `leaflet`

Proportional symbols layer

Gridded Map

Projection

reproducibility

Spatial data and cartography with R

Kim A. Etienne C. Timothée G.

SatRday Paris February 2019

The R Spatial Ecosystem

“Historical” Packages

Simple Features for R

Using sf

Data Import

Projection

Data Display

Centroids Extraction

Distance Matrix

Polygons Aggregation

Buffer Zone

Polygon Intersection

Count Points in Polygons

Other Packages

Maps with R

Overview

Data Preparation

cartography

Proportional symbols

Typology Map

Choropleth Map

Gridded Map

Smoothed Map

Cheat Sheet

ggplot2 and tmap

ggplot2

tmap

mapview and leaflet

Proportional symbols layer

Gridded Map

Projection

reproducibility

Using `sf`

`cartography`

`ggplot2` and `tmap`

`ggplot2`

`tmap`

`mapview` and `leaflet`